Machine learning-based risk stratification for gestational diabetes management
Gestational diabetes mellitus (GDM) is often diagnosed during the last trimester of pregnancy, leaving only a short timeframe for intervention. However, appropriate assessment, management, and treatment have been shown to reduce the complications of GDM. This study introduces a machine learning-base...
| Main Authors: | , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
MDPI
2022
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| _version_ | 1826308461691928576 |
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| author | Yang, J Clifton, D Hirst, J Kavvoura, F Farah, G Mackillop, L Lu, HY |
| author_facet | Yang, J Clifton, D Hirst, J Kavvoura, F Farah, G Mackillop, L Lu, HY |
| author_sort | Yang, J |
| collection | OXFORD |
| description | Gestational diabetes mellitus (GDM) is often diagnosed during the last trimester of pregnancy, leaving only a short timeframe for intervention. However, appropriate assessment, management, and treatment have been shown to reduce the complications of GDM. This study introduces a machine learning-based stratification system for identifying patients at risk of exhibiting high blood glucose levels, based on daily blood glucose measurements and electronic health record (EHR) data from GDM patients. We internally trained and validated our model on a cohort of 1148 pregnancies at Oxford University Hospitals NHS Foundation Trust (OUH), and performed external validation on 709 patients from Royal Berkshire Hospital NHS Foundation Trust (RBH). We trained linear and non-linear tree-based regression models to predict the proportion of high-readings (readings above the UK’s National Institute for Health and Care Excellence [NICE] guideline) a patient may exhibit in upcoming days, and found that XGBoost achieved the highest performance during internal validation (0.021 [CI 0.019–0.023], 0.482 [0.442–0.516], and 0.112 [0.109–0.116], for MSE, R2, MAE, respectively). The model also performed similarly during external validation, suggesting that our method is generalizable across different cohorts of GDM patients. |
| first_indexed | 2024-03-07T07:19:52Z |
| format | Journal article |
| id | oxford-uuid:707d0c23-c95a-4d66-a5ad-cd0842708a67 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:19:52Z |
| publishDate | 2022 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oxford-uuid:707d0c23-c95a-4d66-a5ad-cd0842708a672022-09-21T15:17:39ZMachine learning-based risk stratification for gestational diabetes managementJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:707d0c23-c95a-4d66-a5ad-cd0842708a67EnglishSymplectic ElementsMDPI2022Yang, JClifton, DHirst, JKavvoura, FFarah, GMackillop, LLu, HYGestational diabetes mellitus (GDM) is often diagnosed during the last trimester of pregnancy, leaving only a short timeframe for intervention. However, appropriate assessment, management, and treatment have been shown to reduce the complications of GDM. This study introduces a machine learning-based stratification system for identifying patients at risk of exhibiting high blood glucose levels, based on daily blood glucose measurements and electronic health record (EHR) data from GDM patients. We internally trained and validated our model on a cohort of 1148 pregnancies at Oxford University Hospitals NHS Foundation Trust (OUH), and performed external validation on 709 patients from Royal Berkshire Hospital NHS Foundation Trust (RBH). We trained linear and non-linear tree-based regression models to predict the proportion of high-readings (readings above the UK’s National Institute for Health and Care Excellence [NICE] guideline) a patient may exhibit in upcoming days, and found that XGBoost achieved the highest performance during internal validation (0.021 [CI 0.019–0.023], 0.482 [0.442–0.516], and 0.112 [0.109–0.116], for MSE, R2, MAE, respectively). The model also performed similarly during external validation, suggesting that our method is generalizable across different cohorts of GDM patients. |
| spellingShingle | Yang, J Clifton, D Hirst, J Kavvoura, F Farah, G Mackillop, L Lu, HY Machine learning-based risk stratification for gestational diabetes management |
| title | Machine learning-based risk stratification for gestational diabetes management |
| title_full | Machine learning-based risk stratification for gestational diabetes management |
| title_fullStr | Machine learning-based risk stratification for gestational diabetes management |
| title_full_unstemmed | Machine learning-based risk stratification for gestational diabetes management |
| title_short | Machine learning-based risk stratification for gestational diabetes management |
| title_sort | machine learning based risk stratification for gestational diabetes management |
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